Methodological & Technical Research Topics

LCA with a Distal Outcome

References & recommended reading

Bakk, Z., & Vermunt, J. K. (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling, 23, 20-31. doi: 10.1080/10705511.2014.955104t

Bray, B. C., Lanza, S. T., & Tan, X. (2014). Eliminating bias in classify-analyze approaches for latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal. Advance online publication. doi: 10.1080/10705511.2014.935265.

Dziak, J. J., Bray, B. C., Zhang, J. T., Zhang, M., & Lanza, S. T. (2016). Comparing the performance of improved classify-analyze approaches in latent profile analysis. Methodology: European Journal Of Research Methods For The Behavioral And Social Sciences, 12, 107-116.

Lanza, S. T., Tan, X., & Bray, B. C. (2013). Latent class analysis with distal outcomes: A flexible model-based approach. Structural Equation Modeling: A Multidisciplinary Journal, 20, 1-26.

LCA with Causal Inference

LCA with covariates models the association between classes and predictors, but causation cannot be inferred unless people are randomly assigned to levels of the predictor of latent class membership. Modern causal inference methods, such as inverse propensity weighting, can be used to adjust for potential confounding in observational data. The Methodology Center has pioneered work on applying inverse propensity weights to estimate the causal effects of covariates on latent class membership and to estimate the causal effects of latent class membership on a distal outcome. 

References & recommended reading

Butera, N. M., Lanza, S. T., & Coffman, D. L. (2013). A framework for estimating causal effects in latent class analysis: Is there a causal link between early sex and subsequent profiles of delinquency? Prevention Science. doi: 10.1007/s11121-013-0417-3  PMCID: PMC3888479

Lanza, S.T., Schuler, M.S., & Bray, B.C. (2016). Latent class analysis with causal inference: The effect of adolescent depression on young adult substance abuse profiles. In A. von Eye, & W. Wiedermann (Eds.), Causality and Statistics. Hoboken, NJ: Wiley.

Lanza, S. T., Coffman, D. L., & Xu, S. (2013). Causal inference in latent class analysis. Structural Equation Modeling, 20(3), 361-383. PMCID: PMC4240500

Schuler, M. S., Leoutsakos, J. S., & Stuart, E. A. (2014). Addressing confounding when estimating the effects of latent classes on a distal outcome. Health Services Outcomes and Research Methodology14(4), 232-254.

Latent Class Moderation

Moderation analysis is typically conducted by incorporating a single variable (e.g., gender, baseline severity) as a moderator into a multiple regression model. By using LCA, researchers can identify subgroups of people exposed to a common set of factors, and who, therefore, may respond differently to intervention. That is, latent class membership can be used as the moderating variable in multiple regression. 

References & recommended reading

Cooper, B. R., & Lanza, S. T. (2014). Who benefits most from Head Start? Using latent class moderation to examine differential treatment effects. Child Development. 85(6), 2317-2338.. doi: 10.1111/cdev.12278

Lanza, S. T. & Rhoades, B. L. (2013). Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment. Prevention Science, 14, 157-168. PMCID: PMC3173585

Cleveland, M. J., Lanza, S. T., Ray, A. E., Turrisi, R., & Mallett, K. M. (2012). Transitions in first-year college student drinking behaviors: Does drinking latent class membership moderate the effects of parent- and peer-based intervention components? Psychology of Addictive Behaviors, 26, 440-450. PMCID: PMC3413757

Software Development

We distribute free software to researchers so they can use LCA accurately and easily. The Methodology Center first released PROC LCA for SAS in 2007, and we have regularly added important features to the software.

We also develop macros to enhance PROC LCA functionality.

We also develop LCA software for other platforms.